TY - JOUR

T1 - Risk prediction of product-harm events using rough sets and multiple classifier fusion

T2 - an experimental study of listed companies in China

AU - Wang, Delu

AU - Zheng, Jianping

AU - Ma, Gang

AU - Song, Xuefeng

AU - Liu, Yun

N1 - This is the peer reviewed version of the following article: Wang, D., Zheng, J., Ma, G., Song, X., and Liu, Y. (2016) Risk prediction of product-harm events using rough sets and multiple classifier fusion: an experimental study of listed companies in China. Expert Systems, 33: 254–274. doi: 10.1111/exsy.12148 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/exsy.12148/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.

PY - 2016/6

Y1 - 2016/6

N2 - With the increasing of frequency and destructiveness of product-harm events, study on enterprise crisis management becomes essentially important, but little literature thoroughly explores the risk prediction method of product-harm event. In this study, an initial index system for risk prediction was built based on the analysis of the key drivers of the product-harm event's evolution; ultimately, nine risk-forecasting indexes were obtained using rough set attribute reduction. With the four indexes of cumulative abnormal returns as the input, fuzzy clustering was used to classify the risk level of a product-harm event into four grades. In order to control the uncertainty and instability of single classifiers in risk prediction, multiple classifier fusion was introduced and combined with self-organising data mining (SODM). Further, an SODM-based multiple classifier fusion (SB-MCF) model was presented for the risk prediction related to a product-harm event. The experimental results based on 165 Chinese listed companies indicated that the SB-MCF model improved the average predictive accuracy and reduced variation degree simultaneously. The statistical analysis demonstrated that the SB-MCF model significantly outperformed six widely used single classification models (e.g. neural networks, support vector machine, and case-based reasoning) and other six commonly used multiple classifier fusion methods (e.g. majority voting, Bayesian method, and genetic algorithm).

AB - With the increasing of frequency and destructiveness of product-harm events, study on enterprise crisis management becomes essentially important, but little literature thoroughly explores the risk prediction method of product-harm event. In this study, an initial index system for risk prediction was built based on the analysis of the key drivers of the product-harm event's evolution; ultimately, nine risk-forecasting indexes were obtained using rough set attribute reduction. With the four indexes of cumulative abnormal returns as the input, fuzzy clustering was used to classify the risk level of a product-harm event into four grades. In order to control the uncertainty and instability of single classifiers in risk prediction, multiple classifier fusion was introduced and combined with self-organising data mining (SODM). Further, an SODM-based multiple classifier fusion (SB-MCF) model was presented for the risk prediction related to a product-harm event. The experimental results based on 165 Chinese listed companies indicated that the SB-MCF model improved the average predictive accuracy and reduced variation degree simultaneously. The statistical analysis demonstrated that the SB-MCF model significantly outperformed six widely used single classification models (e.g. neural networks, support vector machine, and case-based reasoning) and other six commonly used multiple classifier fusion methods (e.g. majority voting, Bayesian method, and genetic algorithm).

KW - product-harm

KW - risk prediction

KW - multiple classifiers

KW - self-organising data mining

KW - rough set

U2 - 10.1111/exsy.12148

DO - 10.1111/exsy.12148

M3 - Journal article

VL - 33

SP - 254

EP - 274

JO - Expert Systems

JF - Expert Systems

SN - 0266-4720

IS - 3

ER -